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dc.contributor.authorRodellar Benedé, José
dc.contributor.authorBarrera Llanga, Kevin Iván
dc.contributor.authorAlférez Baquero, Edwin Santiago
dc.contributor.authorBoldú Nebot, Laura
dc.contributor.authorLaguna Moreno, Javier
dc.contributor.authorMolina Borrás, Ángel
dc.contributor.authorMerino González, Anna
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Matemàtiques
dc.date.accessioned2022-07-21T12:06:02Z
dc.date.available2022-07-21T12:06:02Z
dc.date.issued2022-05-23
dc.identifier.citationRodellar, J. [et al.]. A deep learning pproach for the morphological recognition of reactive lymphocytes in patients with COVID-19 infection. "Bioengineering", 23 Maig 2022, vol. 9, núm. 229, p. 1-20.
dc.identifier.issn2306-5354
dc.identifier.urihttp://hdl.handle.net/2117/370833
dc.description.abstractLaboratory medicine plays a fundamental role in the detection, diagnosis and management of COVID-19 infection. Recent observations of the morphology of cells circulating in blood found the presence of particular reactive lymphocytes (COVID-19 RL) in some of the infected patients and demonstrated that it was an indicator of a better prognosis of the disease. Visual morphological analysis is time consuming, requires smear review by expert clinical pathologists, and is prone to subjectivity. This paper presents a convolutional neural network system designed for automatic recognition of COVID-19 RL. It is based on the Xception71 structure and is trained using images of blood cells from real infected patients. An experimental study is carried out with a group of 92 individuals. The input for the system is a set of images selected by the clinical pathologist from the blood smear of a patient. The output is the prediction whether the patient belongs to the group associated with better prognosis of the disease. A threshold is obtained for the classification system to predict that the smear belongs to this group. With this threshold, the experimental test shows excellent performance metrics: 98.3% sensitivity and precision, 97.1% specificity, and 97.8% accuracy. The system does not require costly calculations and can potentially be integrated into clinical practice to assist clinical pathologists in a more objective smear review for early prognosis.
dc.format.extent20 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subject.lcshArtificial intelligence--Engineering applications
dc.subject.lcshCOVID-19 (Disease)
dc.subject.lcshBioengineering
dc.subject.otherDeep learning
dc.subject.otherconvolutional neural networks
dc.subject.otherCOVID-19
dc.subject.otherBlood cell images
dc.subject.otherCell morphology
dc.subject.otherReactive lymphocytes
dc.subject.otherDiagnosis
dc.subject.otherPrognosis
dc.titleA deep learning pproach for the morphological recognition of reactive lymphocytes in patients with COVID-19 infection
dc.typeArticle
dc.subject.lemacIntel·ligència artificial--Aplicacions a l'enginyeria
dc.subject.lemacCOVID-19 (Malaltia)
dc.subject.lemacBioenginyeria
dc.contributor.groupUniversitat Politècnica de Catalunya. CoDAlab - Control, Modelització, Identificació i Aplicacions
dc.identifier.doi10.3390/bioengineering9050229
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://www.mdpi.com/2306-5354/9/5/229
dc.rights.accessOpen Access
local.identifier.drac33756857
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104087RB-I00/ES/HEMATOPATOLOGIA COMPUTACIONAL: SOLUCIONES DE APRENDIZAJE PROFUNDO PARA EL DIAGNOSTICO DE ENFERMEDADES HEMATOLOGICAS A PARTIR DE IMAGENES DE CELULAS DE SANGRE PERIFERICA/
local.citation.authorRodellar, J.; Barrera, K.; Alferez, E.; Boldú, L.; Laguna, J.; Molina, Á.; Merino, A.
local.citation.publicationNameBioengineering
local.citation.volume9
local.citation.number229
local.citation.startingPage1
local.citation.endingPage20


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